MRF undersampling optimization: Software and code for the publication: Mitigating undersampling errors in Magnetic Resonance Fingerprinting by sequence optimization

Magnetic Resonance Fingerprinting (MRF) is an imaging technique for simultaneous estimation of multiple quantitative parameters in a single acquisition. The efficiency of MRF results from strong k-space undersampling, but this can go at the expense of strong aliasing artifacts. The errors due to undersampling can be predicted based on a mathematical model leveraging on perturbation
theory. We exploited this model to perform MRF sequence optimization by adjusting the MRF flip angle train. Numerical simulations showed that the undersampling errors can be suppressed by sequence optimization. This was further corroborated in eight in vivo scans. The results were compared to sequences with a conventionally shaped flip angle pattern and an optimized pattern based on the Cramer-Rao lower bound (CRB). A sequence optimized for improved robustness against undersampling with a flip angle train of length 400 yielded significantly lower median absolute errors T1 (5.6%±2.9%) and T2 (7.9%±2.3%) compared to the conventional (T1: 8.0% ± 1.9%, T2: 14.5% ± 2.6%) and CRB-based (T1: 21.6% ± 4.1%, T2: 31.4% ± 4.4%) sequences. The proposed optimization scheme can be adapted to different scan settings, reference maps and optimization parameters in a straightforward manner.

https://github.com/imphys/MRF_undersampling_optimization